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人机组合的多维任务识别:文献综述

Prakash Baskaran1, Julie A Adams1

  • 1Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR, United States.

Frontiers in robotics and AI
|August 23, 2023
PubMed
概括

机器人需要识别人类队友的任务,以便在动态环境中有效协作. 目前的任务识别方法,通常使用可穿戴传感器,对于复杂,并发的人类活动是不够的.

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人与机器人的交互
  • 人工智能的人工智能
  • 穿戴式计算可以穿戴.

背景情况:

  • 人机团队需要机器人适应人类队友的状态,以便成功合作,特别是在非结构化的环境中.
  • 推断人类队友任务对于机器人适应至关重要,但传统的环境传感器往往是不切实际的.
  • 可穿戴式传感器为动态设置中的任务识别提供了可行的替代方案.

研究的目的:

  • 评估一百多个任务识别算法的可行性,用于在非结构化,动态环境中操作的人机组.
  • 根据它们在多个人类活动组件中识别复合和并发任务的能力来评估算法.
  • 在人机组合的背景下,确定当前任务识别方法的关键局限性.

主要方法:

  • 一个全面的审查和评估超过100个任务识别算法.
  • 评估标准包括敏感性,适用性,概括性,复合因子,并发性和异常意识.
  • 专注于使用可穿戴传感器并能够识别多个活动组件的算法.

主要成果:

  • 大多数审查的任务识别算法不适合在非结构化,动态环境中的人机器人团队.
  • 现有的算法通常只从活动组件的子集 (例如,运动,认知,言语) 中检测任务.
关键词:
活动识别活动识别人与机器人的合作.机器学习是机器学习.任务识别 任务识别可以穿戴的传感器.

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  • 很少有算法可以同时识别跨多个活动组件的复合和并发任务.

    • 结论:

    • 当前的任务识别算法在很大程度上无法满足复杂的现实场景中人机器人合作的需求.
    • 对于能够使用可穿戴传感器数据推断复合,并发的人类任务的先进算法有很大的需求.
    • 开发强大的任务识别能力对于实现自主机器人适应和有效的人机协作至关重要.